What are the differences between NVIDIA A100 and H100 GPUs?
Key Differences Between NVIDIA A100 and H100 GPUs
When comparing NVIDIA's powerful GPUs, the A100 and H100 are among the most advanced units designed for data centers, AI training, and high-performance computing (HPC). Both GPUs offer exceptional performance, but notable differences exist in architecture, performance, and technological features. Below, we break down these differences into clear sections.
GPU Architecture Comparison: Ampere vs. Hopper
- NVIDIA A100: Built on NVIDIA's Ampere architecture, the A100 GPU was introduced in 2020. It includes third-generation Tensor Cores, Multi-Instance GPU (MIG) technology, and NVLink connectivity to enhance scalability and AI workloads.
- NVIDIA H100: Based on the newer Hopper architecture introduced in 2022, the H100 GPU brings significant improvements, including fourth-generation Tensor Cores, Transformer Engine, and enhanced MIG capabilities.
Performance and Specifications
| Specification | NVIDIA A100 | NVIDIA H100 |
|---|---|---|
| Architecture | Ampere | Hopper |
| Manufacturing Process | 7nm TSMC | 4nm TSMC |
| GPU Memory | 40GB/80GB HBM2e | 80GB HBM3 |
| Memory Bandwidth | Up to 2039 GB/s | Up to 3 TB/s |
| FP64 Compute | 9.7 TFLOPS | ~60 TFLOPS |
| FP32 Compute | 19.5 TFLOPS | ~60 TFLOPS |
| FP16 Tensor Core | 312 TFLOPS | Up to 2000 TFLOPS (with Transformer Engine) |
| NVLink Bandwidth | 600 GB/s | 900 GB/s |
| Interconnect Technology | NVLink & PCIe Gen4 | NVLink 4.0 & PCIe Gen5 |
Memory and Bandwidth Improvements
The NVIDIA H100 GPU introduces significant memory improvements over the A100 GPU. The H100 uses high-bandwidth memory generation 3 (HBM3), delivering a massive bandwidth increase (approximately 3 TB/s) over the A100's HBM2e (around 2 TB/s). This enhanced memory performance allows for faster data transfers, benefiting AI training and large-scale HPC applications.
Tensor Core and AI Performance
Tensor Core performance sees substantial improvements with the H100 GPU. Hopper architecture introduces fourth-generation Tensor Cores and a new Transformer Engine, specifically optimized for transformer models commonly used in workloads like GPT-style language models. These enhancements enable the H100 GPU to perform significantly faster than the A100 GPU in AI training and inference tasks, particularly in scenarios involving large language models and transformers.
Multi-Instance GPU (MIG) Enhancements
Both GPUs support NVIDIA's Multi-Instance GPU (MIG) technology, enabling users to partition GPU resources into multiple isolated instances. However, the H100 has improved MIG capabilities, allowing more flexibility and greater resource utilization, resulting in enhanced efficiency in multi-user environments and cloud infrastructure.
Connectivity and Scalability Improvements
The NVIDIA H100 GPU supports NVLink 4.0 and PCIe Gen5, significantly improving data transfer speeds and inter-GPU connectivity compared to the A100. NVLink 4.0 delivers 900 GB/s bandwidth, offering a 50% increase over the A100's NVLink bandwidth (600 GB/s). These improvements facilitate better scalability and more efficient GPU-to-GPU communication, essential in large-scale AI training and HPC clusters.
Use Cases: When to Choose A100 vs. H100 GPUs?
Choose NVIDIA A100 if:
- Budget constraints are significant, as the A100 GPU is typically less expensive.
- Workloads are already optimized and stable on Ampere architecture.
- Immediate availability and compatibility with current infrastructure are key considerations.
Choose NVIDIA H100 if:
- You require cutting-edge performance for advanced AI, deep learning, and large-scale HPC workloads.
- Transformer-based models (e.g., GPT models, BERT, T5) are central to your workflows.
- You have workloads that would greatly benefit from the increased memory bandwidth and improved Tensor Core performance.
- Future-proofing infrastructure and scalability are high priorities.
Conclusion: Which GPU is Better?
The NVIDIA H100 GPU offers significant advancements over the A100 GPU, including substantial improvements in compute performance, memory bandwidth, Tensor Core capabilities, and scalability features. For organizations needing the highest performance possible, especially in AI training, transformer-based workloads, and advanced HPC applications, the H100 is the better choice.
However, the A100 remains a powerful, cost-effective GPU, suitable for a wide range of enterprise AI and HPC workloads that do not necessarily require the absolute latest technology.
Ultimately, the choice between NVIDIA A100 and H100 GPUs depends on your workload types, performance requirements, and budget considerations.